Module 1, Lecture 1: Bioinformatics

COMP-364 Tools for the Life Sciences

www.bci.mcgill.ca My lab website
www.bci.mcgill.ca/home/?page_id=811 Course Website

MTR 10:35am-11:25am
ENGTR – Trottier Building 2120
Jan 7th 2016 – April 15th 2016

\( {\tt michael.t.hallett@mcgill.ca} \)
Office: McIntyre 903
Office Hours: TBA

Mohamed Ghadie, Teaching Assistant
\( {\tt mohamed.ghadie@mail.mcgill.ca} \)
Office: TBA
Office Hours: TBA

• If you are registered for this course, you will have a SOCS (School of Comp Sci) account.
• The SOCS account provides you access to the SOCS workstations and server.
• However, your laptop or workstation at home should suffice for the course.
• I can provide additional instruction if someone needs it regarding these machines.

• There is some focus on the basic biology of breast cancer.
• There is also a focus on statistics and computation to examine these biologies.
• This requires that you learn some statistics and how they are used to explore systems biology and -omic data.
• This is a heavy programming course, if you have never programmed before.
• But you will know how to program at the end of it.

• We are mostly going to use data made available as part of the Breast Cancer TCGA dataset.
• You will also need to become familiar with RStudio, a programming environment to deal with R and datasets.
• You will also have to become a little bit familiar with a software versioning system called GIT and a website that allows us to save/distribute/manage GIT repositories called BitBucket.
• We will make all the slides, data, code and assignments available via a GIT & BitBucket.

• Bioinformatics is an experimental science.
• This means that you need to sit down and do experiments.
• The primary means of doing experiments in bioinformatics is through the use of statistics and computation.
• This requires programming.
• Learning to program is like learning a language.
• It is an investment, and I motivate why this investment will change your career.
• Practice and all is coming.

• The science of biological information
• Managing biological information is a part of bioinformatics but not all
• Examples:
  • The use tools from information science to collect and organize biological informatics.
  • e.g. www.yeastgenome.org/ websites that provide comprehensive perspectives of an organism.
  • e.g. Efforts like that NCBI www.ncbi.nlm.nih.gov that contains many different bioinformatics objects like
    • PubMed www.ncbi.nlm.nih.gov/pubmed for publications of manuscripts,
    • GenBank www.ncbi.nlm.nih.gov/genbank that collects all genes
    • dbGaP www.ncbi.nlm.nih.gov/gap that collects all genotype/phenotype relationships that have been discovered.

• Bioinformatics tools such as PubMed, GenBank, dbGap and others facilitate the study of specific genes and gene products by life scientists.
• Consider what life science research looked like circa 1990 (25 years ago).
• At that time, the vast majority of basic life sci researchers studied a single gene (or gene product), or at most a single complex (e.g. ribosome).
• In '90, How did an ESR (Estrogen Receptor) researcher “track” new results?
• Internet popularized in ~'92. PubMed released in '96. GenBank '88. dbGap '07.
• A lot of actual walking to a library and looking up keywords at the back of a journal that seemed likely to publish results about ESR.
• That is, it was the dark ages.

• Then came the internet in '92. General interconnectivity. Within 5 years, every major journal was publishing their papers on-line.
• Then came PubMed.
• Now virtually every published medical/life science paper was available instantly.
• The ability to search text for keywords (eg ESR, estrogen) within english text allowed single genes/gene products to be followed closely.

• Daily email updates for a keyword search.

• So Pubmed allowed researchers to identify papers that mentioned a gene (eg ESR). A lot of Principle Investigators (PIs) still primarily and only use PubMed to track their genes.

• But what about results related to ESR that are derived from -omic/systems biology efforts.

• Eg. every time an individual is sequenced, their ESR gene is sequenced and any mutations in this gene add to the global pool of polymorphisms.

• Eg. every time a higher-order eukaroyte is sequenced, a homologue of ESR is sequenced (and may not be named ESR).

• [But what about results related to ESR that are derived from -omic/systems biology efforts.]

• Eg. every time a gene expression microarray is performed on a human sample, ESR levels are of course measured, since these microarrays cover the complete transcriptome. How can we get this information?

• Eg. every time a mass spectrometry experiment is performed to identify proteins or protein interactions, ESR will have be measured too.

• How can we explore this information? Is there an equivalent of PubMed?

• [How can we explore this information? Is there an equivalent of PubMed?]

• First off, how many such studies are there? 1 per year? 5 per year?

• My daily NCBI automated search for the keyword “breast cancer” retrieves ~15 articles per day.

  • Keyword “genomics” retrives ~90 articles per day.
  • keyword “next generation sequencing” retrieves ~40 per day.

• The science of biological information

• Managing biological information is a part of bioinformatics but not all

• Bioinformatics is also the investigation of biological systems using tools from information science.

• For example, my lab considers itself to be a breast cancer research lab whose primary assay is bioinformatics (as opposed to pull downs, PCR, microarray or other assays).

• Often this is about hypothesis testing and biomarker discovery.

• For example, the development of gene panels like Oncotype DX www.oncotypedx.com

• And often this is about model building (examples in next slides)

• For example, models of the genome, exome, transcriptome, proteome, protein interactome, methylomes, epigenome, … and many other -omic entities.

• Hypothesis testing, biomarkers, and model building all require a tremendous amount of tools from biostatistics and computation.

The HapMap project that catalogs single nucleotide polymorphisms and other mutations in human populations.

Gene Expression Omnibus and other efforts seek to catalogue transcriptional (mRNA expression levels)

• The above examples weren't really bioinformatics specific, but rather genomics, proteomics, systems biology or other -omic challenges.
• These are in large part about technology (to sequence in a massively parallel fashion, mass spectrometry for proteomics/metabolomics, microscopy, other screens …)
• However they do use the cataloging/organizational aspects that bioinformatics offers.
• In addition to simply collecting and organizing information, the main aim of bioinformatics is to model biological processes…
• … often using the information provided by these -omic projects.

• The science of biological information
• Managing biological information is a part of bioinformatics but not all
• Bioinformatics is also a predictive science:

Can we build a model that accurately predicts how a biological system will behave?

The list goes on and on:

• the genomic organization of a gene,
• alternative splicing,
• almost every biological process or response (reponse to stress, starvation, heat shock, cold shock, hypoxia, …),
• protein translation,
• degradation,
• protein trafficking,
• \( \ldots \)